Full Stack Data Engineer

As a Data Engineer, you will design and scale robust batch and streaming data pipelines on the Google Cloud Platform (GCP). You will build exceptional analytical data products rooted in solid data warehousing principles.

Working in an agile, customer-centric environment, you will champion modern software engineering practices. You will manage and scale our platform's infrastructure using Terraform (Infrastructure as Code) and continuously deliver high-quality code through robust CI/CD pipelines. As collaboration is key, you will partner closely with analytics stakeholders to streamline how data is acquired, processed, and presented.

Security and quality are built into everything we do. You will proactively monitor data quality, document data lineage, and address code vulnerabilities using modern Dev Sec Ops  tools. Additionally, you will optimize our existing data solutions to ensure they are highly secure, reliable, cost-effective, and performing at their peak, while providing critical production support to keep our pipelines running smoothly.

At Ford, we are transforming the future of mobility, and data is the engine driving that transformation. The Finance Data Hub is at the center of this evolution, modernizing how Ford manages, analyzes, and leverages financial data globally. We are looking for a talented and driven Data Engineer to join our product team. In this role, you will build the scalable, high-performance data pipelines and cloud infrastructure that power critical finance initiatives and strategic decision-making across the enterprise.

* Pipeline Development & Ingestion:
Design, build, and scale robust batch and streaming data pipelines on Google Cloud Platform (GCP) to process large volumes of finance data.

* Data Warehousing & Architecture:
Develop exceptional analytical data products applying solid data warehouse principles, data modeling, and best practices.

* Infrastructure & Dev Ops:
Maintain and enhance the platform's infrastructure using Terraform (Infrastructure as Code) and continuously develop, evaluate, and deploy code using CI/CD pipelines.

* Stakeholder

Collaboration:

Partner closely with data analytics stakeholders to streamline and optimize data acquisition, processing, and presentation workflows.

* Data Governance & Quality:
Implement and promote enterprise data governance models focusing on data protection, sharing, reuse, standards, quality monitoring, and data lineage documentation.

* Code Quality & Security:
Write clean, reliable code using Test-Driven Development (TDD) in an agile environment, actively addressing security vulnerabilities and code quality issues using tools like Sonar Qube, Checkmarx, Fossa, and Cycode.

* Optimization & Cost Management:
Continuously optimize existing data solutions (pipelines, infrastructure, and products) to ensure high performance, security, reliability, low vulnerability, and cost efficiency.

* Production Support:
Monitor production pipelines and provide timely production support to resolve issues in accordance with established SLAs.

* Continuous Improvement:
Stay current on modern data engineering practices, contribute to the company's technical direction, and proactively build domain expertise in finance data.

* Pipeline Development & Ingestion:
Design, build, and scale robust batch and streaming data pipelines on Google Cloud Platform (GCP) to process large volumes of finance data.

* Data Warehousing & Architecture:
Develop exceptional analytical data products applying solid data warehouse principles, data modeling, and best practices.

* Infrastructure & Dev Ops:
Maintain and enhance the platform's infrastructure using Terraform (Infrastructure as Code) and continuously develop, evaluate, and deploy code using CI/CD pipelines.

* Stakeholder

Collaboration:

Partner closely with data analytics stakeholders to streamline and optimize data acquisition, processing, and presentation workflows.

* Data Governance & Quality:
Implement and promote enterprise data governance models focusing on data protection, sharing, reuse, standards, quality monitoring, and data lineage documentation.

* Code Quality & Security:
Write clean, reliable code using Test-Driven Development (TDD) in an agile environment, actively addressing security vulnerabilities and code quality issues using tools like Sonar Qube, Checkmarx, Fossa, and Cycode.

* Optimization & Cost Management:
Continuously optimize existing data solutions (pipelines, infrastructure, and products) to ensure high performance, security, reliability, low vulnerability, and cost efficiency.

* Production Support:
Monitor production pipelines and provide timely production support to resolve issues in accordance with established SLAs.

* Continuous Improvement:
Stay current on modern data engineering practices, contribute to the company's technical direction, and proactively build domain expertise in finance data.